A Practical Digital Signal Carrier Acquisition based on Kalman Filter Theory
Bibliographic record
Abstract
Traditionally Extended Kalman Filter has been proposed and studied for the acquisition of a digital carrier signal. However, this technique imposes a heavy load on the processor. And high performances cannot be expected for the poor linearity, in most cases. To overcome these problems, this paper shows that one can use a simple, purely linear Kalman Filter which consists of two states variables - phase and frequency. This new technique selects the carrier phase as the input to the filter, instead of a pair of orthogonal signal amplitudes. The filtering logic is made up of only 4 additions and 2 multiplications. The results of both simulations and experiments show that this filter can acquire the carrier signal within 10 symbols with a probability of 98 % during the initial phase, even when the frequency offset is as large as 20 % of the symbol rate frequency at C/N=6dB. In the steady state, the measured BER is close to the theoretical values. While delivering a similar performance mentioned above, this filter can operate even when the carrier frequency deviates from the expected figure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".